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import streamlit as st
import plotly.express as px
import numpy as np
import plotly.graph_objects as go
from sklearn.metrics import r2_score
from collections import OrderedDict
import pickle
import json
import streamlit as st
import plotly.express as px
import numpy as np
import plotly.graph_objects as go
from sklearn.metrics import r2_score
import pickle
import json
import pandas as pd
import statsmodels.api as sm
from sklearn.metrics import mean_absolute_percentage_error
import sys
import os
from utilities import (set_header,
initialize_data,
load_local_css,
create_channel_summary,
create_contribution_pie,
create_contribuion_stacked_plot,
create_channel_spends_sales_plot,
format_numbers,
channel_name_formating,
load_authenticator)
import seaborn as sns
import matplotlib.pyplot as plt
import sweetviz as sv
import tempfile
original_stdout = sys.stdout
sys.stdout = open('temp_stdout.txt', 'w')
sys.stdout.close()
sys.stdout = original_stdout
st.set_page_config(layout='wide')
load_local_css('styles.css')
set_header()
for k, v in st.session_state.items():
if k not in ['logout', 'login','config'] and not k.startswith('FormSubmitter'):
st.session_state[k] = v
authenticator = st.session_state.get('authenticator')
if authenticator is None:
authenticator = load_authenticator()
name, authentication_status, username = authenticator.login('Login', 'main')
auth_status = st.session_state.get('authentication_status')
if auth_status == True:
is_state_initiaized = st.session_state.get('initialized',False)
if not is_state_initiaized:
a=1
def plot_residual_predicted(actual, predicted, df_):
df_['Residuals'] = actual - pd.Series(predicted)
df_['StdResidual'] = (df_['Residuals'] - df_['Residuals'].mean()) / df_['Residuals'].std()
# Create a Plotly scatter plot
fig = px.scatter(df_, x=predicted, y='StdResidual', opacity=0.5,color_discrete_sequence=["#11B6BD"])
# Add horizontal lines
fig.add_hline(y=0, line_dash="dash", line_color="darkorange")
fig.add_hline(y=2, line_color="red")
fig.add_hline(y=-2, line_color="red")
fig.update_xaxes(title='Predicted')
fig.update_yaxes(title='Standardized Residuals (Actual - Predicted)')
# Set the same width and height for both figures
fig.update_layout(title='Residuals over Predicted Values', autosize=False, width=600, height=400)
return fig
def residual_distribution(actual, predicted):
Residuals = actual - pd.Series(predicted)
# Create a Seaborn distribution plot
sns.set(style="whitegrid")
plt.figure(figsize=(6, 4))
sns.histplot(Residuals, kde=True, color="#11B6BD")
plt.title(' Distribution of Residuals')
plt.xlabel('Residuals')
plt.ylabel('Probability Density')
return plt
def qqplot(actual, predicted):
Residuals = actual - pd.Series(predicted)
Residuals = pd.Series(Residuals)
Resud_std = (Residuals - Residuals.mean()) / Residuals.std()
# Create a QQ plot using Plotly with custom colors
fig = go.Figure()
fig.add_trace(go.Scatter(x=sm.ProbPlot(Resud_std).theoretical_quantiles,
y=sm.ProbPlot(Resud_std).sample_quantiles,
mode='markers',
marker=dict(size=5, color="#11B6BD"),
name='QQ Plot'))
# Add the 45-degree reference line
diagonal_line = go.Scatter(
x=[-2, 2], # Adjust the x values as needed to fit the range of your data
y=[-2, 2], # Adjust the y values accordingly
mode='lines',
line=dict(color='red'), # Customize the line color and style
name=' '
)
fig.add_trace(diagonal_line)
# Customize the layout
fig.update_layout(title='QQ Plot of Residuals',title_x=0.5, autosize=False, width=600, height=400,
xaxis_title='Theoretical Quantiles', yaxis_title='Sample Quantiles')
return fig
def plot_actual_vs_predicted(date, y, predicted_values, model):
fig = go.Figure()
fig.add_trace(go.Scatter(x=date, y=y, mode='lines', name='Actual', line=dict(color='blue')))
fig.add_trace(go.Scatter(x=date, y=predicted_values, mode='lines', name='Predicted', line=dict(color='orange')))
# Calculate MAPE
mape = mean_absolute_percentage_error(y, predicted_values)*100
# Calculate R-squared
rss = np.sum((y - predicted_values) ** 2)
tss = np.sum((y - np.mean(y)) ** 2)
r_squared = 1 - (rss / tss)
# Get the number of predictors
num_predictors = model.df_model
# Get the number of samples
num_samples = len(y)
# Calculate Adjusted R-squared
adj_r_squared = 1 - ((1 - r_squared) * ((num_samples - 1) / (num_samples - num_predictors - 1)))
metrics_table = pd.DataFrame({
'Metric': ['MAPE', 'R-squared', 'AdjR-squared'],
'Value': [mape, r_squared, adj_r_squared]})
fig.update_layout(
xaxis=dict(title='Date'),
yaxis=dict(title='Value'),
title=f'MAPE : {mape:.2f}%, AdjR2: {adj_r_squared:.2f}',
xaxis_tickangle=-30
)
return metrics_table,fig
# # Perform linear regression
# model = sm.OLS(y, X).fit()
eda_columns=st.columns(3)
with eda_columns[0]:
tactic=st.checkbox('Tactic Level Model')
if tactic:
with open('mastercard_mmm_model.pkl', 'rb') as file:
model = pickle.load(file)
train=pd.read_csv('train_mastercard.csv')
test=pd.read_csv('test_mastercard.csv')
train['Date']=pd.to_datetime(train['Date'])
test['Date']=pd.to_datetime(test['Date'])
train.set_index('Date',inplace=True)
test.set_index('Date',inplace=True)
test.dropna(inplace=True)
X_train=train.drop(["total_approved_accounts_revenue"],axis=1)
y_train=train['total_approved_accounts_revenue']
X_test=test.drop(["total_approved_accounts_revenue"],axis=1)
X_train=sm.add_constant(X_train)
X_test=sm.add_constant(X_test)
y_test=test['total_approved_accounts_revenue']
# sys.stdout.close()
# sys.stdout = original_stdout
# st.set_page_config(layout='wide')
# load_local_css('styles.css')
# set_header()
channel_data=pd.read_excel("Channel_wise_imp_click_spends_new.xlsx",sheet_name='Sheet3')
target_column='Total Approved Accounts - Revenue'
with eda_columns[1]:
if st.button('Generate EDA Report'):
def generate_report_with_target(channel_data, target_feature):
report = sv.analyze([channel_data, "Dataset"], target_feat=target_feature,verbose=False)
temp_dir = tempfile.mkdtemp()
report_path = os.path.join(temp_dir, "report.html")
report.show_html(filepath=report_path, open_browser=False) # Generate the report as an HTML file
return report_path
report_file = generate_report_with_target(channel_data, target_column)
if os.path.exists(report_file):
with open(report_file, 'rb') as f:
st.download_button(
label="Download EDA Report",
data=f.read(),
file_name="report.html",
mime="text/html"
)
else:
st.warning("Report generation failed. Unable to find the report file.")
st.title('Analysis of Result')
st.write(model.summary(yname='Revenue'))
metrics_table_train,fig_train= plot_actual_vs_predicted(X_train.index, y_train, model.predict(X_train), model)
metrics_table_test,fig_test= plot_actual_vs_predicted(X_test.index, y_test, model.predict(X_test), model)
metrics_table_train=metrics_table_train.set_index('Metric').transpose()
metrics_table_train.index=['Train']
metrics_table_test=metrics_table_test.set_index('Metric').transpose()
metrics_table_test.index=['test']
metrics_table=np.round(pd.concat([metrics_table_train,metrics_table_test]),2)
st.markdown('Result Overview')
st.dataframe(np.round(metrics_table,2),use_container_width=True)
st.subheader('Actual vs Predicted Plot Train')
st.plotly_chart(fig_train,use_container_width=True)
st.subheader('Actual vs Predicted Plot Test')
st.plotly_chart(fig_test,use_container_width=True)
st.markdown('## Residual Analysis')
columns=st.columns(2)
Xtrain1=X_train.copy()
with columns[0]:
fig=plot_residual_predicted(y_train,model.predict(Xtrain1),Xtrain1)
st.plotly_chart(fig)
with columns[1]:
st.empty()
fig = qqplot(y_train,model.predict(X_train))
st.plotly_chart(fig)
with columns[0]:
fig=residual_distribution(y_train,model.predict(X_train))
st.pyplot(fig)
else:
with open('mastercard_mmm_model_channel.pkl', 'rb') as file:
model = pickle.load(file)
train=pd.read_csv('train_mastercard_channel.csv')
test=pd.read_csv('test_mastercard_channel.csv')
# train['Date']=pd.to_datetime(train['Date'])
# test['Date']=pd.to_datetime(test['Date'])
# train.set_index('Date',inplace=True)
# test.set_index('Date',inplace=True)
test.dropna(inplace=True)
X_train=train.drop(["total_approved_accounts_revenue"],axis=1)
y_train=train['total_approved_accounts_revenue']
X_test=test.drop(["total_approved_accounts_revenue"],axis=1)
X_train=sm.add_constant(X_train)
X_test=sm.add_constant(X_test)
y_test=test['total_approved_accounts_revenue']
channel_data=pd.read_excel("Channel_wise_imp_click_spends_new.xlsx",sheet_name='Sheet3')
target_column='Total Approved Accounts - Revenue'
with eda_columns[1]:
if st.button('Generate EDA Report'):
def generate_report_with_target(channel_data, target_feature):
report = sv.analyze([channel_data, "Dataset"], target_feat=target_feature)
temp_dir = tempfile.mkdtemp()
report_path = os.path.join(temp_dir, "report.html")
report.show_html(filepath=report_path, open_browser=False) # Generate the report as an HTML file
return report_path
report_file = generate_report_with_target(channel_data, target_column)
# Provide a link to download the generated report
with open(report_file, 'rb') as f:
st.download_button(
label="Download EDA Report",
data=f.read(),
file_name="report.html",
mime="text/html"
)
st.title('Analysis of Result')
st.write(model.summary(yname='Revenue'))
metrics_table_train,fig_train= plot_actual_vs_predicted(X_train.index, y_train, model.predict(X_train), model)
metrics_table_test,fig_test= plot_actual_vs_predicted(X_test.index, y_test, model.predict(X_test), model)
metrics_table_train=metrics_table_train.set_index('Metric').transpose()
metrics_table_train.index=['Train']
metrics_table_test=metrics_table_test.set_index('Metric').transpose()
metrics_table_test.index=['test']
metrics_table=np.round(pd.concat([metrics_table_train,metrics_table_test]),2)
st.markdown('Result Overview')
st.dataframe(np.round(metrics_table,2),use_container_width=True)
st.subheader('Actual vs Predicted Plot Train')
st.plotly_chart(fig_train,use_container_width=True)
st.subheader('Actual vs Predicted Plot Test')
st.plotly_chart(fig_test,use_container_width=True)
st.markdown('## Residual Analysis')
columns=st.columns(2)
Xtrain1=X_train.copy()
with columns[0]:
fig=plot_residual_predicted(y_train,model.predict(Xtrain1),Xtrain1)
st.plotly_chart(fig)
with columns[1]:
st.empty()
fig = qqplot(y_train,model.predict(X_train))
st.plotly_chart(fig)
with columns[0]:
fig=residual_distribution(y_train,model.predict(X_train))
st.pyplot(fig)
elif auth_status == False:
st.error('Username/Password is incorrect')
if auth_status != True:
try:
username_forgot_pw, email_forgot_password, random_password = authenticator.forgot_password('Forgot password')
if username_forgot_pw:
st.success('New password sent securely')
# Random password to be transferred to user securely
elif username_forgot_pw == False:
st.error('Username not found')
except Exception as e:
st.error(e)
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